Introduction: This analysis performs a gene-gene clustering procedure that will identify clusters of co-expressed genes across multiple sample groups. It first runs an ANOVA to find genes significantly changed across sample groups and uses these genes as seeds to initiate a number of gene clusters. These clusters will be further refined based on several user-specific paramters. Gene set enrichment analysis is then used to find pre-defined gene sets that are over-represented in each cluster.
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Perturbed rhythmic activation of signaling pathways in mice deficient for Sterol Carrier Protein 2-dependent diurnal lipid transport and metabolism. (GSE67426)
Jouffe C, Gobet C, Martin E, Métairon S et al. Perturbed rhythmic activation of signaling pathways in mice deficient for Sterol Carrier Protein 2-dependent diurnal lipid transport and metabolism. Sci Rep 2016 Apr 21;6:24631. PMID: 27097688.
Comparison of liver mRNA expression from Scp2 KO and wild-type mice harvested every 2 hours during 3 consecutive days.
Circadian rhythm in mouse liver, wild type mice only, 3 replicates every 2 hours (12 groups). Each gene was adjusted to its 0 hour mean and rescaled to make its standard deviation equal to 0. As a demonstration, only a subset of genes in the original data with high between sample variance were used.
The input data matrix was normalized using sample group WT_00Hr as control, so, the data of each gene was substracted by control group mean and had SD equal to 1.0
Figure 1. Principal components analysis (PCA) using all genes. Samples were colored by their groups.
Summary statistics and ANOVA p value across all sample groups were calculated for each gene.
Figure 2. Distribution of ANOVA p values. 821 genes have p values less than 0.01.
Differentially expressed genes (DEGs) were selected as seeds for generating gene clusters, using the following criteria:
A total of 232 genes were selected. These genes would be used as seeds to generate gene clusters in the next step.
Figure 3. Hierarchical clustering of samples using all genes (unsupervised) or selected DEGs (supervised).
Gene clusters were identified from the DEG seeds with the following steps:
8 gene clusters of 221 genes were identified from 232 seed DEGs.
Figure 4. Color of each block corresponds to the average expression (normalized) of each initial gene cluster in each sample (red = higher).
The gene clusters identified from the DEG seeds were further refined with the following steps:
The reclustering didn’t converge after 20 cycles
A total of 554 genes were clustered after refinement.
Figure 5. Color of each block corresponds to the average expression (normalized) of each refined gene cluster in each sample (red = higher).
More info:
Table 1 Summary of individual clusters, with the average expression (normalized) of all genes of each cluster in all sample groups. Click on cluster name for visualization of each cluster: 1). the average and standard error of sample averages in each group; 2). hierarchical sample clustering using all genes of the cluster; and 3). heatmap of all genes and samples of the cluster.
| Cluster | Num_Gene | Mean_WT_00Hr | Mean_WT_02Hr | Mean_WT_04Hr | Mean_WT_06Hr | Mean_WT_08Hr | Mean_WT_10Hr | Mean_WT_12Hr | Mean_WT_14Hr | Mean_WT_16Hr | Mean_WT_18Hr | Mean_WT_20Hr | Mean_WT_22Hr |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cluster_1 | 74 | 0 | 0.320 | 0.4003 | 0.4754 | -0.0674 | -0.8074 | -1.3232 | -1.7725 | -1.4775 | -0.8675 | -0.4452 | 0.038 |
| Cluster_2 | 92 | 0 | 0.220 | 0.6953 | 1.1131 | 1.3442 | 0.7731 | 0.5681 | -0.5761 | -1.1411 | -0.8391 | -0.4592 | -0.140 |
| Cluster_3 | 76 | 0 | 0.027 | 0.4720 | 0.9529 | 1.6852 | 1.7297 | 1.8217 | 1.0513 | 0.1103 | -0.0200 | -0.1501 | -0.150 |
| Cluster_4 | 68 | 0 | -0.110 | -0.2174 | 0.0500 | 0.8026 | 1.5266 | 1.9326 | 1.9415 | 1.4404 | 1.0700 | 0.7313 | 0.170 |
| Cluster_5 | 22 | 0 | -0.910 | -1.3292 | -1.3408 | -1.3086 | -0.8028 | -0.5605 | 1.1441 | -0.4487 | -0.9260 | -1.4410 | -0.760 |
| Cluster_6 | 41 | 0 | 0.120 | -0.3093 | -1.0027 | -1.6774 | -1.6972 | -1.4500 | -0.3144 | 0.4795 | 0.2814 | -0.7223 | -0.760 |
| Cluster_7 | 81 | 0 | -0.360 | -0.7090 | -0.8816 | -1.0076 | -0.4168 | -0.1783 | 0.7138 | 1.1981 | 1.1456 | 0.8200 | 0.290 |
| Cluster_8 | 100 | 0 | -0.110 | -0.3746 | -0.7491 | -1.5633 | -1.8686 | -1.9389 | -1.6773 | -0.7202 | -0.1745 | 0.1049 | 0.150 |
Find predefined gene sets enriched in gene cluster comparing to the background.
Table 2 Numbers of predefined gene sets significantly enriched in each gene cluster. Gene sets were split based on their sources, such as the NCBI BioSystems and KEGG databases. Click on each number to see list of the gene sets.
Figure 6. Plot the patterns of all clusters using the mean and standard error of samples of each group.
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Figure 7. Color represents the average expression (normalized across samples) of all genes in the same cluster and all samples in the same group (red = higher).
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Figure 8. Color represents the average expression (normalized across samples) of individual genes in all samples of the same group (red = higher).
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Figure 9. Color represents the expression level (normalized across samples) of each gene and each sample (red = higher).
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Figure 10. Color represents the size of each cluster (number of genes) after a reclustering cycle.
Check out the RoCA home page for more information.
To reproduce this report:
Find the data analysis template you want to use and an example of its pairing YAML file here and download the YAML example to your working directory
To generate a new report using your own input data and parameter, edit the following items in the YAML file:
Run the code below within R Console or RStudio, preferablly with a new R session:
if (!require(devtools)) { install.packages('devtools'); require(devtools); }
if (!require(RCurl)) { install.packages('RCurl'); require(RCurl); }
if (!require(RoCA)) { install_github('zhezhangsh/RoCAR'); require(RoCA); }
CreateReport(filename.yaml); # filename.yaml is the YAML file you just downloaded and edited for your analysis
If there is no complaint, go to the output folder and open the index.html file to view report.
## R version 3.2.2 (2015-08-14)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.5 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] rchive_0.0.0.9000 gplots_3.0.1 htmlwidgets_0.8
## [4] DT_0.2 RoCA_0.0.0.9000 awsomics_0.0.0.9000
## [7] RCurl_1.95-4.8 bitops_1.0-6 devtools_1.13.2
## [10] yaml_2.1.13 rmarkdown_1.3 knitr_1.14
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.12 magrittr_1.5 highr_0.6
## [4] stringr_1.2.0 caTools_1.17.1 tools_3.2.2
## [7] KernSmooth_2.23-15 withr_1.0.2 htmltools_0.3.5
## [10] gtools_3.5.0 rprojroot_1.2 digest_0.6.12
## [13] formatR_1.4 memoise_1.1.0 evaluate_0.9
## [16] gdata_2.17.0 stringi_1.1.1 backports_1.1.0
## [19] jsonlite_1.0
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